Three recent papers from David Baker (co-winner of the 2004 Foresight Institute Feynman Prize for Theory) and his colleagues document progress in protein design as a path to advanced nanotechnology. One recommendation of the Technology Roadmap for Productive Nanosystems is to support the development of modular molecular composite nanosystems (MMCNs), in which a million-atom-scale biomolecular framework (usually made from DNA) is used to organize functional nanoscale components of various types for various purposes. Potentially one of the richest sources of such functional nanoscale components is the on-going protein design revolution.
One of the properties of proteins that makes them so useful as functional nanoscale components is that protein surfaces can bind to other protein surfaces in a defined geometry to form structurally-defined assemblies of two or more proteins joined at specific interfaces. A paper “A De Novo Protein Binding Pair By Computational Design and Directed Evolution” published in Molecular Cell [abstract, PDF (1.2 MB)] demonstrated a computational method to design new protein-protein complexes between two noninteracting protein partners. The synthetic protein pair bound with high affinity, which was further improved by nearly three orders of magnitude by directed evolution. This achievement points to the possibility of engineering unrelated proteins with complementary activities to form a multi-protein complex with a desired composite function.
An important component of protein design is a large collection of known protein structures upon which protein structure models can be based. X-ray crystallographic methods for experimentally determining protein structure are impressive, but have difficulty solving some structures. A May paper “Improved molecular replacement by density- and energy-guided protein structure optimization” in Nature [abstract, PDF (973 KB)] from Dr. Baker and his colleagues combined computation models of protein structure with methods for determining protein structure by X-ray crystallography. From a University of Washington news release written by Leila Gray:
The structures of many protein molecules remain unsolved even after experts apply an extensive array of approaches. An international collaboration has led to a new, high-performance method that rapidly determined the structure of protein molecules in several cases where previous attempts had failed. …
“The important new method described this week in Nature highlights the value of computational modeling in helping scientists to determine the structures and functions of molecules that are difficult to study using current techniques,” said Dr. Peter Preusch, who oversees Baker’s research grant and other structural biology grants at the National Institutes of Health (NIH). “Expanding the repertoire of known protein structures — a key goal of the NIH Protein Structure Initiative, which helped fund the research – will be of great benefit to scientists striving to design new therapeutic agents to treat disease.” …
To test the performance of their new integrated method, the researchers looked at 13 sets of X-ray crystallography data on molecules whose structures could not be solved by expert crystallographers. These structures remained unsolved even after the application of an extensive array of other approaches. The new integrated method was able to yield high resolution structures for 8 of these 13 highly challenging models.
“The results show that structural prediction methods such as Rosetta can be even more powerful when combined with X-ray crystallography data,” the researchers noted. They added that the integrated approach probably outperforms others because it provides physical chemistry and protein structural information that can guide the massive sampling of candidate configurations. This information eliminates most conformations that are not physically possible.
Our procedures, the authors noted, required considerable computation, as up to several thousand Rosetta model predictions are generated for each structure. The researchers have developed automated procedures that potentially could narrow down the possibilities and lessen the number of times a model is rebuilt to make corrections. This automation could reduce computing time.
The design of novel protein-protein interactions published in Molecular Cell above is greatly extended in a Science paper from Dr. Baker and his colleagues, in which they describe a general computational method for designing proteins that bind a surface path of interest on a target molecule. In contrast to the previous two-sided approach in which both members of a pair of non-interacting proteins were engineered to bind to each other, in this case only one protein is designed to bind the target surface. As with the two papers above, the complete text of “Computational Design of Proteins Targeting the Conserved Stem Region of Influenza Hemagglutinin” [abstract] can be downloaded from the Baker Lab web site (PDF, 644 KB). Both the advances and limitations of this research are described in a post on the Rosetta Design Group blog “One Sided De-Novo Computational Design of a Protein-Protein Interaction“:
Fleishman et al. describe a general computational method for designing proteins that bind a surface patch of interest on a target macromolecule. They first identify good binding sites for ‘anchor’ residues on the target surface and utilize these to anchor de novo designed interfaces. The method was used to design proteins that bind a conserved surface patch on the stem of the influenza hemagglutinin (HA) from the 1918 H1N1 pandemic virus. Two of several tens of designs showed very nice binding, reaching (after affinity maturation) low nanomolar affinity. One of the designs was shown to inhibit the HA fusogenic conformational changes induced at low pH. The crystal structure of the designed protein in complex with HA revealed an actual binding interface nearly identical to that in the computational prediction. Such designed binding proteins may be useful in the future for both diagnostics and therapeutics. …
My impression is that we need different strategies to tackle the many different types of interactions observed in nature. We have only addressed the issue of sidechain-dominated interactions. How to design backbone-mediated interactions, as well as highly polar and charged interactions remains unanswered. Other issues are what is it that makes a protein bind to its target, which is intimately related to the question of our low success rate in de novo design.
For a discussion of the significance for atomically precise building block assembly of this latest advance in designing tight binding in a specific geometry, see this post on Eric Drexler’s blog.
